AIAED-19: AI + Adaptive Education Beijing, China, May 24-25, 2019

Conference website	http://www.aiaed.net
Submission link	https://easychair.org/conferences/?conf=aiaed19
Abstract registration deadline	April 1, 2019
Submission deadline	April 16, 2019

AIAED 2019 is an international forum for academic and industry researchers to discuss important trends emerging from artificial intelligence, machine learning, data mining, natural language processing, multimodal analytics, and system architecture as applied to the field of next-generation education and how these advances can impact adaptive human learning at scale and in various contexts. This conference provides researchers and product developers an opportunity to exchange information and ideas on related research, development, and applications.

Submission Guidelines

AIAED 2019 welcomes extended abstracts on applications of AI in adaptive education as well as novel crosscutting work in related areas. Submissions will be peer-reviewed and are expected to demonstrate rigorous methodology or theoretical positions. 

Abstracts should be a single page with 250 words in the main body and a single figure or table that best illustrates your theory, methodology, and/or findings.

​Extended abstracts should be 1000-1500 words with the following six sections: Introduction (100-150 words), Relevant Theories of Learning (200-300 words), Enabling Technological Advances (200-300 words), Real World Applications (200-300 words), Evidence of Potential Impacts (200-300 words), and Summary (100-150 words). Extended abstracts should have a maximum of 20 references (in addition to the 1000-1500 words). 

All abstracts and extended abstracts should be submitted through EasyChair.

Submissions should be formatted according to the following templates: 

• Abstract template (250 words)

• Extended abstract template (1000-1500 words)

Selection Criteria

Abstracts are required for scholarship selection. Early submissions receive prioritized funding support.

Extended abstracts are reviewed based on the following criteria:

• Theoretical foundation: Does the literature review provide relevant theories of learning and a strong theoretical basis for your work?

• Technological advances and technical soundness: How does your work enable technological advances and/or how rigorous are your methodologies?

• Real world application: What changes in educational activities that were realized from your work or could be realistically derived from your work?

• Potential impact: What is the value and potential impact of your contribution to education at scale?

• Presentation: How clear, concise, and comprehensible is your presentation?

Suggested Topics

• Applications of machine learning, deep learning, and/or educational data mining to adaptive education
• Applications of multimodal integrated behavioral and affective analytics to education
• Applications of natural language processing and semantic analysis to education
• Applications of image recognition and processing to adaptive education
• AI-based applications in K12 practices
• Interaction of AI and learning science/engineering
• Standards and infrastructure for the development of AI-based adaptive education, including contributions toward IEEE Adaptive Instructional System standards and IEEE federated machine learning standards
• Self-improvement in adaptive learning systems

Publication

AIAED-19 proceedings will be published online by AIAED and will be made freely available under a Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

After the conference, (1) accepted submissions will be invited to contribute full papers to special issues of academic journals (more info to come); (2) selected submissions will be invited to contribute full papers for inclusion in the book publication on the state of the art of AI in adaptive education.

Venue

The conference will be held in Beijing, China, on May 24 - 25.

Contact

All questions about submissions should be emailed to info@AIAED.net.

Sponsors

This conference is co-organized by the IEEE Learning Technology Standards Committee (LTSC) and Squirrel AI Learning, and is co-sponsored by the IEEE Adaptive Instructional Systems (AIS) Working Group, IEEE IC Industry Consortium on Learning Engineering (ICICLE), and IEEE Federated Machine Learning Work Group. This conference is endorsed and supported by the International Artificial Intelligence in Education (IAIED) Society.